Adaptive guassian derivative sigma systems and methods

ABSTRACT

In one embodiment, a method is provided. The method comprises determining a first value of a coefficient of an edge-determining algorithm in response to a spatial resolution of a first image acquired with an image capture device onboard a vehicle, a spatial resolution of a second image, and a second value of the coefficient in response to which the edge-determining algorithm generated a second edge map corresponding to the second image. The method further comprises determining, with the edge-determining algorithm in response to the coefficient having the first value, at least one edge of at least one object in the first image. The method further comprises generating, in response to the determined at least one edge, a first edge map corresponding to the first image. The method further comprises determining at least one navigation parameter of the vehicle in response to the first and second edge maps.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is related to patent application Ser. No. ______(Attorney Docket No. 400.2369), titled IMPROVED EDGE DETECTION VIAWINDOW GRID NORMALIZATION, filed ______, the contents of which arehereby incorporated by reference in their entirety.

BACKGROUND

Vehicles, such as manned and unmanned aircraft, can include an onboardInertial Navigation System (hereinafter “INS”) that includes one or moreInertial Measurement Units (hereinafter “IMU”) to determine and to trackone or more of vehicle position, velocity, and attitude (e.g., pitch,roll, yaw).

A potential problem with using an IMU in an INS is that the IMU cansuffer from accumulated error, which leads to the navigation parameters(e.g., position, velocity, attitude) that the INS estimates from the IMUoutput values “drifting” relative to the actual navigation parameters.That is, the respective difference between each estimated and actualnavigation parameter accumulates such that over time, the error betweenthe estimated and actual parameter values increases and can becomesignificant if not corrected on a periodic or other basis.

One way to periodically correct the estimated navigation parameters isto use a Global Navigation Satellite System, such as a GlobalPositioning System (hereinafter “GPS”) to determine accurate values ofthe navigation parameters, and to feed the accurate, or truth, parametervalues to, e.g., a Kalman filter, which alters the coefficients that thefilter uses to estimate navigation parameters such as position,velocity, and attitude so that these navigation parameters will, overtime, converge to accurate values. That is, the Kalman filter uses theGPS values not only to correct the estimated navigation parameters atone time, but to make more accurate the algorithm that estimates thenavigation parameters.

Unfortunately, because it may be subject to geographic outages and othertimes of unavailability caused by, e.g., spoofing or jamming of GPSsignals, GPS may, at certain times, be unavailable for use by an INS tocorrect the estimates made in response to an IMU.

Therefore, a need has arisen for a technique and system that allows forcorrection, periodic or otherwise, of navigation parameters estimated byan INS in response to navigation data generated by an IMU even when GPSis unavailable.

SUMMARY

In one embodiment, a method is provided. The method comprisesdetermining a first value of a coefficient of an edge-determiningalgorithm in response to a spatial resolution of a first image acquiredwith an image-capture device onboard a vehicle, a spatial resolution ofa second image, and a second value of the coefficient in response towhich the edge-determining algorithm generated a second edge mapcorresponding to the second image. The method further comprisesdetermining, with the edge-determining algorithm in response to thecoefficient having the first value, at least one edge of at least oneobject in the first image. The method further comprises generating, inresponse to the determined at least one edge, a first edge mapcorresponding to the first image; and determining at least onenavigation parameter of the vehicle in response to the first and secondedge maps.

The details of one or more embodiments are set forth in the descriptionbelow. The features illustrated or described in connection with oneexemplary embodiment may be combined with the features of otherembodiments. Thus, any of the various embodiments described herein canbe combined to provide further embodiments. Aspects of the embodimentscan be modified, if necessary to employ concepts of the various patents,applications and publications as identified herein to provide yetfurther embodiments. Other features, objects and advantages will beapparent from the description, the drawings, and the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

Exemplary features of the present disclosure, its nature and variousadvantages will be apparent from the accompanying drawings and thefollowing detailed description of various embodiments. Non-limiting andnon-exhaustive embodiments are described with reference to theaccompanying drawings, wherein like labels or reference numbers refer tolike parts throughout the various views unless otherwise specified. Thesizes and relative positions of elements in the drawings are notnecessarily drawn to scale. For example, the shapes of various elementsmay be selected, enlarged, and positioned to improve drawing legibility.The particular shapes of the elements as drawn may have been selectedfor ease of recognition in the drawings. One or more embodiments aredescribed hereinafter with reference to the accompanying drawings inwhich:

FIG. 1 is a diagram that demonstrates a method for correlating remotesensing images based on the position of a vehicle that acquired theimages, according to an embodiment.

FIG. 2 is a remote sensing image and a remote sensing edge map generatedin response to the remote sensing image, according to an embodiment.

FIG. 3 is a flow chart of a correlation algorithm for comparing edgefeatures in a remote sensing-model image to the edge features extractedfrom an acquired image, according to an embodiment.

FIGS. 4A and 4B are diagrams that illustrate determining the cameraextent of a camera onboard the aircraft at various angles, according toan embodiment.

FIG. 5 is a flow chart of a process for generating an edge map inresponse to images acquired by a vehicle, and for storing the edge map,according to an embodiment.

FIG. 6 is a flow chart of a process for adjusting one or more parametersof an edge-detection algorithm based on a ratio of the spatialresolutions of a remote sensing image and a camera image, according toan embodiment.

FIG. 7 is a block diagram of a comparison algorithm for comparing edgefeatures of a georeferenced ortho-rectified satellite image and a cameraimage to correct navigation data, according to an embodiment.

FIG. 8 is a flow chart of a comparison algorithm for comparing edgefeatures of a georeferenced ortho-rectified satellite image and a cameraimage to correct navigation data, according to an embodiment.

FIGS. 9A and 9B are, respectively, an edge map normalized over theentire image generated from an acquired image of a region of terrain,and an edge map of the same acquired image normalized over one or morewindow grids, according to an embodiment.

FIG. 10 is a block diagram of vehicle that includes a navigationsubsystem configured for adjusting one or more parameter of anedge-detection algorithm based on the ratio of the spatial resolutionsof a remote sensing image and a camera image, according to anembodiment.

DETAILED DESCRIPTION

In the following detailed description, reference is made to theaccompanying drawings that form a part hereof, and in which is shown byway of illustration specific illustrative embodiments. However, it is tobe understood that other embodiments may be utilized, and that logical,mechanical, and electrical changes may be made. Furthermore, the methodpresented in the drawing figures and the specification is not to beconstrued as limiting the order in which the individual steps may beperformed. The following detailed description is, therefore, not to betaken in a limiting sense.

The following disclosure is directed to related improvements in vehiclenavigation technology. One set of embodiments relates to normalizing oneor more features of an acquired remote sensing image to represent thedimensions of the vehicle's point-of-view, and to storing an edge mapthat is generated in response to the normalized image. And a second setof embodiments discloses adjusting one or more parameters of anedge-detection algorithm based on the spatial resolutions of the storededge-map image and an acquired remote sensing image to better detectterrain edges, and using a resulting edge map to correctnavigation-tracking errors. As a result, the disclosed embodiments allowfor improved accuracy of edge-detection algorithms and navigationsystems. Additionally, embodiments disclosed herein allow for increasedversatility in navigation systems, as the image-capture device used toacquire remote sensing images from a vehicle, such as an aircraft, notonly can include an one or more optical cameras, but also can include,in addition to or instead of one or more optical cameras, other sensorssuch as infrared thermal imagers and millimeter-wave radar sensors.

Unless otherwise stated, terms in this disclosure are intended to conveytheir ordinary meaning as understood by those skilled in the art. Forexample, use of the word “vehicle” would include, but would not belimited to, air vehicles (e.g., aircraft), land vehicles, watervehicles, motor vehicles, and space vehicles. An aircraft is depicted inthe accompanying drawings and used throughout the disclosure simply forpedagogical reasons.

The embodiments described herein generally enable the determination ofthe position of a vehicle based on the comparison (e.g., alignment) ofan image taken from an image capture device onboard the vehicle (such asa camera) with a georeferenced, orthorectified remote sensing image ofthe area below the vehicle. A camera mounted to the vehicle acquires afirst image of the area below the vehicle, in which the image has afirst set of dimensions (e.g. image size and spatial resolution). Then,using an estimated position of the vehicle via a navigation system (suchas an INS), a second image of the area corresponding to the estimatedposition of the vehicle is acquired externally via a georeferencedorthorectified imagery (remote sensing) database.

A first set of embodiments disclosed herein enable the generation of anedge map database from normalized images obtained externally from thevehicle, each image with dimensions matching dimensions of an imageacquired with an image-capture device onboard the vehicle. Furthermore,the first set of embodiments can enable comparison, e.g., in the form ofspatial resolution alignment, of an edge map corresponding to a secondimage taken by an image-capture device onboard the vehicle, whichcomparison may aid in vehicle-position correction as described below. Inan example of the first set of embodiments, using a navigation systemonboard the vehicle (e.g. an INS or GNSS system), a first image isobtained from a georeferenced, orthorectified imagery database using theposition data gathered by the navigation system. From the first image,an image portion is extracted having the dimensions of an extent of asecond image-capture device onboard the vehicle. Said another way, thedimensions of one image (such as a imagery acquired by a satellite) areadjusted to match the dimensions of the extent of a camera onboard thevehicle, where the extent of the camera is the dimensions of the surfacearea that the image acquired by the onboard camera represents.

One or more image portions (e.g. rectangular grids) from the first,database image are normalized to match, as closely as possible, thespatial resolution of the corresponding image portion(s) of the imageacquired by the vehicle image-capture device for better edge detection.An edge detection algorithm (e.g. Canny edge detection) is applied tothe normalized image portion. The resulting edge image then can bestored in a georeferenced edge-map database for later use, such as fordetermining vehicle position as illustrated in the second set ofembodiments. Utilizing a georeferenced edge map database enables thedetermination of the vehicle position via correlating techniques asdescribed below.

A second set of embodiments describe techniques for correcting estimatednavigation data based on adjusting one or more parameters of an edgedetection algorithm. A first image having a first set of spatialresolutions (e.g. ground sample distances) is acquired externally fromthe vehicle, such as from a georeferenced, orthorectified imagerydatabase. A second image having a second set of spatial resolutions isacquired from an onboard vehicle camera.

However, the vehicle-acquired image may not represent the same spatialresolution(s) as the image acquired via the georeferenced,orthorectified imagery database. In that instance, the spatialresolution(s) represented by the vehicle-acquired image are adjusted toalign with the spatial resolution(s) represented by the database image.The adjustment is made by scaling one or more parameters (e.g. gaussianblurs) of an edge detection algorithm with a ratio of the spatialresolutions between the georeferenced, orthorectified imagery databaseimage and the vehicle-acquired image. Even if the spatial resolutionsare not equal, scaling the one or more parameters increases thecompatibility of the edges detected in the vehicle-acquired image withthe edges in the stored database image. Since the georeferenced,orthorectified imagery database includes position data (such as lateraland longitudinal coordinates and a known reference frame) correspondingto the image, the position of the vehicle (e.g. location, heading) canbe more accurately determined from aligning the vehicle-acquired imageto the image acquired from the georeferenced, orthorectified imagerydatabase. That is, by aligning the vehicle-acquired image (whose precisereference frame and associated position data is still unknown) with adatabase image (whose reference frame and position data are known, i.e.,georeferenced), the INS onboard the vehicle can make a more preciseposition determination. This information can then be used to correctestimated position information calculated by the vehicle.

Yet, while possible, directly aligning two images taken with differentimage-capture devices at different heights/altitudes (e.g., withdifferent spatial resolutions) above the ground can be a difficult task.Therefore, in an embodiment, an edge image of the vehicle-acquired imageis generated and compared with a generated edge map corresponding to thegeoreferenced, orthorectified imagery database. The edge map of thegeoreferenced, orthorectified imagery database image can be obtained,for example, via the edge map database described with respect to thefirst set of embodiments above. Alignment of the aerial edge image withthe edge map, rather than the images themselves, may facilitatecomparison between the two images (and thus may provide improvedposition determination). This is because edges tend to be sensoragnostic for a variety of sensors.

FIG. 1 is a set of diagrams that together provide a general overview ofthe first and second set of embodiments. FIG. 1 includes ground image101, with shapes (square, triangle, and circle) representing, forpurposes of example, significant features (e.g., mountains, bodies ofwater, fields, forests, man-made structures) in the ground image. Groundimage 101 may be acquired, for example, by a satellite and stored in animagery database (not shown in FIG. 1).

However, a potential problem with using ground-map images for navigationis that the dimensions (e.g., area) of the terrain represented by (e.g.,visible in) a ground-map image acquired by a satellite may not beequivalent to the dimensions of the ground represented by an imageacquired by a camera, or other image-acquisition device, onboard avehicle such as an aircraft. Said another way, the spatial resolution ofthe ground-map image may be different from the spatial resolution of thevehicle-acquired image, where spatial resolution is a measure of how thedimensions of the terrain represented by the image relates to thedimensions of the image. This relationship can be defined as the groundsample distance (GSD) of the image, where the GSD is a representation ofthe distance (e.g. meters) between two neighboring pixels in an image.An example of such spatial resolution would be an image having a GSD of10 centimeters (cm), which would correlate to each pixel in the imagerepresenting 10 cm on the ground. Throughout this disclosure, thespatial resolution of an image is referred to as the GSD of the image.

Furthermore, the extent of the camera onboard the aircraft representsthe dimensions of the section of terrain of which the camera acquires animage. These dimensions can be represented by optical resolution(measured in pixels) and a GSD (measured in distance units). Forexample, if the optical resolution of a camera is 1000×2000 pixels, andthe GSD is 10 meters, then the extent of the camera relative to theterrain is 10 kilometers by 20 kilometers; that is, the image acquiredby the camera represents 200 km² of the terrain. So the extent of thecamera depends on the size and resolution of the image it acquires (asdescribed below, ground sample distance depends on the height of thecamera about the ground when the camera captures the image of theterrain). Therefore, so that the acquired image and database image canbe properly aligned to determine navigation parameters, in an embodimentthe navigation system compares an image acquired from a vehicle with aportion of an edge map database image having the same GSD and resolutionas the acquired image, and therefore, having the same extent as theimage-capture device onboard the vehicle. Moreover, a vehicle may not bepositioned parallel with the ground or with the samenorth-south-east-west (NSEW) orientation as the database image;therefore, an image acquired via the vehicle camera may be orienteddifferently than an image from the terrain-map database. This isillustrated in FIG. 1, where a vehicle 104 acquires a terrain image 102at an orientation that is different than that of terrain image 101.

The present disclosure describes a technique for adjusting an imageparameter of the ground image 101 such that at least a portion of theadjusted ground image has approximately the same extent as theimage-capture device onboard the vehicle, and thus has approximately thesame spatial resolution and dimensions as the acquired ground image.This is depicted in FIG. 1, where a satellite image of a ground region(e.g., ground image 101) effectively is scaled to have a spatialresolution and dimensions that match, approximately, the spatialresolution and dimensions, respectively, of the image 102 of the sameground region taken with an image-capture device onboard the vehicle104, and where at least one of the images is re-oriented to align withthe other image. The scaled and re-oriented ground image 103 isillustrated as the dashed diagram of FIG. 1. Although the dashed terrainimage 103 is shown in FIG. 1 as having dimensions slightly larger thanthe solid diagram it represents, this enlargement is merely forillustrative purposes and one skilled in the art will recognize that inpractice the scaled and re-oriented ground image 103 will have, at leastideally, identical, or substantially identical, spatial resolution,dimensions, and orientation as the ground image 101. The scaled andre-oriented ground image 103 is then compared with the ground image 101to determine position information corresponding to the aligned groundimage 103 based on the known position information associated with groundimage 101 as described below in conjunction with FIGS. 6-8. For example,by determining the amount of scaling needed to match the extent of anaircraft-acquired image to the extent of the database image (orvice-versa), and the amount of rotation needed to align theaircraft-acquired image with the database image, a navigation system candetermine coordinates, heading, and altitude of the aircraft.

As described above, it may be easier to align edge maps of therespective images instead of the images themselves. Therefore, in anembodiment, an edge map is generated from the vehicle-acquired image andthe database image before alignment and comparison of the two maps. Inanother embodiment, an edge map corresponding to a portion of ageoreferenced, orthorectified database image matching the extent of avehicle image-capture device is stored in an edge-map database beforecomparison.

FIG. 2 provides a general illustration of generating an edge map from aremote sensing ground image, according to an embodiment. That is, anexample of an edge map generated from a ground image is portrayed inFIG. 2. A ground image 201 represents an image acquired via either asatellite or an image-capture device, for example a camera, onboard anaircraft. An edge map 202 represents edges (illustrated as white linesin the edge map 202) of significant features of the ground image 201,such features including roads, buildings, and rivers, the edges detectedby a circuit, such as a microprocessor or microcontroller, executingimage-processing software to implement an embodiment of anedge-detection technique as described below.

An embodiment for generating an edge map database of one or more imageportions of a georeferenced, orthorectified database image are describedin conjunction with FIGS. 3-5.

FIG. 3 is a diagram 300 of an algorithm for generating an edge map, suchas the edge map 202 of FIG. 2, from an acquired image of a region ofground, such as the acquired image 201 of FIG. 2, according to anembodiment. The algorithm can be executed, or otherwise performed, by ahardware circuit configured by firmware or executing software-programinstructions stored on non-transitory computer-readable media such asnon-volatile memory.

FIG. 4 are diagrams demonstrating the extent of a vehicle image-capturedevice, such as a camera, and how it relates to an image plane of thecamera.

FIG. 5 is a flow diagram 500 of a process for acquiring a georeferenced,orthorectified-database image using navigation data obtained by thevehicle and based on the camera of the vehicle image-capture device,normalizing one or more image portions of the database image, andgenerating an edge map of the normalized database image.

At a step 501, the ground image, which was acquired a priori from animage-capture device (e.g., a camera onboard a satellite and not shownin FIGS. 2-3), is obtained from, for example, a database. The databaseincludes associated position data that is used to describe the location(e.g., reference point) in which the image was taken, such as ageoreferenced, orthorectified imagery database. For ease of explanation,such a database will be referred to as a remote sensing database. Theimage-capture device can be, or can include, for example, any optical orelectromagnetic sensor, or a camera. For illustrative purposes, althoughit is assumed hereinafter that the terrain image is acquired via anunmanned aerial vehicle (e.g. satellite), the ground image may also beacquired via other means.

At a step 502, one or more image portions are extracted from the groundimage, each of the one or more image portions having approximately thesame extent of a vehicle image-capture device, such as a camera, otheroptical sensor, or other electromagnetic sensor). The vehicleimage-capture device may be located on, within, or mounted to thevehicle. For illustrative purposes, the vehicle image-capture device isdescribed as a camera.

Referring to FIGS. 3-4, selecting a portion of a ground image 501 suchthat the portion approximates the extent of the vehicle image-capturedevice is described, according to an embodiment. Said another way,extracting, from the ground image 201, the appropriate image portionsize of the ground image(s) so that the image portion matches the extentof the vehicle image-capture device.

Referring now to FIG. 3, which is a diagram 300 of an algorithm that onecan use to execute the flow diagram of FIG. 5, an INS navigationestimator 312 estimates the vehicle's altitude.

Next, a height-AGL converter 305 converts the determined altitude to anestimated height-above-ground level (AGL) measurement via a ground model301. For example, if the vehicle's estimated altitude is 10,000 feet,but the ground model shows that the vehicle is located over a1000-foot-above-sea-level plateau, then the vehicle's height aboveground level is 10,000 feet-1000 feet-9000 feet. Altitude as used hereinmeans the vertical distance between the vehicle and sea level, andheight above ground level means the vertical distance between thevehicle and the section of ground below the vehicle. Therefore, theground model 301 includes elevations of land and man-made formationsthat form, or that are otherwise part of, the terrain. As describedabove, height AGL is determined by subtracting the height of the vehicleabove the land elevation from the altitude of the vehicle.

Then a camera-extent calculator 406 calculates the extent 404 of thecamera onboard the vehicle in response to the estimated height AGL ofthe onboard camera and the known (typically rectangular) dimensions ofthe camera's image sensor and the focal length of the camera's lens.Said another way, the calculator 406 can determine the extent of thecamera in response to the height AGL and the camera's field of view(sometimes called field of regard). Camera extent as used herein meansthe dimensions of the portion of the ground that appears in an image ofthe terrain captured by the onboard camera. For illustrative purposes,the portion of the ground of which the camera captures an image isassumed to be flat, i.e., planar, though the camera-extent determiner306 also can calculate the camera extend for terrain that is not flat.

FIG. 4 illustrates an example calculation of the camera extent that thecamera-extent determiner 306 is configured to perform. FIG. 4A displaysa situation in which the camera onboard the vehicle is parallel to theground plane, while FIG. 4B displays the situation where the camera ispositioned at an angle relative to the ground; however, one withordinary skill in the art will recognize that the mathematicalprinciples described with respect to FIG. 4 apply or can be modified inboth situations. The camera extent 404 of the camera 402 onboard vehicle401 is represented by the shaded rectangle, which forms the base of arectangular pyramid. Camera 402 is positioned at the bottom of vehicle401, though camera 402 can be mounted to vehicle 401 in other ways (e.g.on the tail, wing, or fuselage of vehicle 401). Image plane 403corresponds to the “active” planar area of the camera's pixel-arraysensor that is configured to capture the incident light from which thecamera generates the pixels that form the captured image (it is assumedthat the camera is a digital camera), which is magnified in at FIG. 4for clarity. By using the similar triangles theorem, the camera cornerscan be projected onto the ground plane. Using principles of Euclideangeometry, the scope of the camera field-of-view can be determined by thefollowing equation (hereinafter “Equation 1”):

$P_{Corner}^{NED} = {{AGL*\frac{\Delta P_{Corner}^{NED}}{u_{z}^{T}\Delta P_{Corner}^{NED}}} + P_{FP}^{NED}}$

where P_(Corner) ^(NED) is the camera corner as projected onto theground in the NED reference frame, AGL is the altitude above groundlevel, ΔPP_(Corner) ^(NED) is the vector from the camera focal point tothe camera corner in the NED frame, u_(z) is the unit column vector inthe z axis, and P_(FP) ^(NED) is the origin of the camera coordinateframe, which corresponds to the camera focal point. ΔP_(Corner) ^(NED)is determined by the following equation:

ΔP _(Corner) ^(NED) =R _(Camera) ^(NED) *K−1*P _(Corner) ^(VIP)

where R_(Camera) ^(NED) is the rotation from camera coordinate frame toNED coordinate frame, K is the camera intrinsic matrix obtained duringcamera calibration, and P_(Corner) ^(VIP) is the camera corner in theimage reference frame (in pixels). An example camera intrinsic matrix K,and its associated inverted matrix, can be illustrated as:

${K = \begin{bmatrix}f_{x} & s & c_{x} \\0 & f_{y} & c_{y} \\0 & 0 & 1\end{bmatrix}},{K^{- 1} = \begin{bmatrix}\frac{1}{f_{x}} & {- \frac{s}{f_{x}f_{y}}} & \frac{{c_{y}z} - {c_{x}f_{y}}}{f_{x}f_{y}} \\0 & \frac{1}{f_{y}} & {- \frac{c_{y}}{f_{y}}} \\0 & 0 & 1\end{bmatrix}}$

where f_(x) and f_(y) are the x and y focal lengths in pixels, s is askew value (generally equivalent to zero), and c_(x) and c_(y) is theprincipal point (i.e., optical axis intersection with the image plane).The four camera corner values (P_(Corner) ^(VIP)) can also berepresented by a matrix, for example, the matrix illustrated below:

$\begin{bmatrix}x_{c} \\y_{c} \\1\end{bmatrix} = \begin{bmatrix}0 & {w - 1} & {w - 1} & 0 \\0 & 0 & h & h \\1 & 1 & 1 & 1\end{bmatrix}$

where x_(c) and y_(c) are the x and y axis values of each of the fourcamera corners, w is the width (length in x coordinates) of the camera,and h is the height (length in y coordinates). Ultimately, the cameraextent can be used to determine the ground sample distance of the imageacquired by the vehicle image capture device, which can be furthercompared with the georeferenced, orthorectified database image asdescribed in further detail below. Once the camera extent has beendetermined, one or more portions of the ground image 201 are extractedto have dimensions matching the camera extent of camera 402.

Referring back to FIG. 5, process 500 continues to a step 503, where theportion(s) of the ground image 201 that has the determined camera extentis normalized based on a parameter of the image portion. Normalizationcan include normalizing the GSD of the captured image to the GSD of thedatabase image, so that images of like GSD are compared, if the capturedand database images do not already have approximately the same GSD.Furthermore, for example, the image portion can be normalized by thecontrast (e.g. the differences in light and color) of the image portionbased on techniques known in the art. The size of each pixel in an imagedepends on the spatial resolution of the image, which in turn depends onthe height of the vehicle that captured the image. Thus, normalizing animage can provide greater clarity of the image, which can aid inidentifying edge features for edge map generation.

However, the effectivity of normalization largely depends on thecontrast of the region that is normalized, due to the difference ingradient magnitude over the normalization area. As the variance ofcontrast of the normalized area decreases, the detection of edgessimilarly decreases. Thus, in a region that includes both high contrastand low contrast portions, normalization over the entire region willaccentuate only the high contrast portions, which can lead to a lowerquality edge map. However, if the region is further sub-divided suchthat the high and low contrast portions are separated into differentimage portions, and normalization is performed for each portionseparately, then contrast variance can be evaluated separately for eachimage portion. By normalizing over smaller image portions, smallervariances in contrast can be accentuated, which ultimately means moreedges can be detected, thus improving the quality of the edge detectionprocess.

This relationship is illustrated in FIG. 9, which shows two generatededge maps 901 and 902 (FIGS. 9A and 9B, respectively) from remotesensing images that have been normalized. FIG. 9A displays an edge map901 where the input image has been normalized over a large area (forexample, over the entirety of the image). As a result, smaller edges maybe diminished or eliminated altogether, which may result in a distortedor lesser quality edge map. In contrast, FIG. 9B displays an edge map902 in which the input image is sectioned into grids of sub-images andnormalization is performed on each sub-image. Because normalization isperformed over a smaller variance in contrast, the generated edge mapshown at 902 contains increased edge features. Thus, normalization isperformed over the remote sensing image in smaller sub-images such thatmore edge features can be detected. The size of each sub-image may bethe matching dimensions of the vehicle camera 402 but may also be biggeror smaller depending on the precise embodiment.

Referring back to FIG. 5, one or more edges are detected from thenormalized image portion(s) from the georeferenced, orthorectifieddatabase at step 504. Edge detection can be realized through techniquesknown in the art, but in one embodiment, edge detection is realizedusing a modified Canny edge-detection algorithm.

At a step 505, an edge map is generated based on the detected one ormore edges of the normalized image portion from the georeferenced,orthorectified database.

Process 500 ends at step 506, where the edge map is stored in a database(e.g. in a second remote sensing database) for future use, such as forcorrecting navigation estimation data as further described below. Thesecond remote sensing database thus includes edge maps generated fromnormalized images acquired from the first remote sensing databasedescribed above. Until retrieval, the edge map can be stored in acompressed file format, such as a portable network graphic (PNG) file.

FIGS. 3-4 and 6-10 describe techniques for implementing the second setof embodiments. These embodiments relate to processing an image acquiredby an image-capture device (e.g. camera) coupled to (e.g. mounted on) avehicle, such as an aircraft, to generate an edge map of the image.Additionally, a second edge map generated a priori, for exampleaccording to the above-described first set of embodiments, is retrievedfrom a remote sensing image database, for example, the second remotesensing database, and compared with the edge map of the image acquiredvia the vehicle image-capture device. The generation of the first edgemap (from the image acquired by the vehicle camera) is done by adjustingone or more terms, e.g., coefficients or other parameters, of anedge-detection algorithm, for example Canny edge detection, such thatthe terms correspond to a ratio of the ground sample distance of thedatabase image and the image acquired by the vehicle camera. From there,the two edge maps are compared to determine one or more navigationparameters of the vehicle, which can be used to correct initialnavigation estimates in times where other position determininginformation (such as GPS data) is unavailable.

FIG. 6 is a flow diagram 600 of a process for determining at least onenavigation parameter from two compared edge maps corresponding to avehicle location. As with respect to FIG. 5, the process represented bythe flow diagram 600 is meant to be construed as illustrative and notintended to be limiting; thus, some steps in flow diagram 600 may beperformed out of the described sequence.

Referring to FIG. 6, at a step 601, an image is acquired from animage-capture device (e.g. camera) onboard a vehicle. In this example,the acquired image is of the area below the vehicle. The image acquiredby the vehicle image-capture device has a corresponding ground sampledistance.

Then, at a step 602, circuitry, such as a microprocessor ormicrocontroller of a navigation system, determines a first value of anedge-detecting algorithm coefficient in response to the image acquiredvia the vehicle image capture device. The first value relates to anintrinsic feature of the image. For example, in an embodiment, the firstvalue relates to the ground sample distance of the image. If Canny edgedetection is performed on the image (as described in further detailbelow), then the coefficient is a sigma (σ) value of the image based atleast in part on the first value, wherein the sigma value is a gaussianblur factor intrinsic to the vehicle-acquired image.

Proceeding to a step 603, an image (edge) map is acquired, where theedge map is based on a second image acquired by a second image-capturedevice. For purposes of illustration, the second image-capture devicecan be a camera mounted a remote sensing vehicle (e.g. satellite), butother image sensors and vehicles may be used to acquire the secondimage. The edge map image may be retrieved from the second remotesensing database described above, but other edge map databases may beused. The retrieval of the edge map is made based on the currentposition of the vehicle. Referring back to FIG. 3, INS navigationestimator 312 (from an INS/IMU) provides an initial estimate of thevehicle's position, which is then used to calculate the altitude of thevehicle as described above. The altitude measurement is converted to aheight AGL measurement using the land elevation data provided by groundmodel 301, and the camera extent of the vehicle image capture device iscalculated based on the computed AGL measurement using the techniquesdescribed with respect to FIG. 3. Once the camera extent of the vehicleimage capture device has been determined, the algorithm 300 acquires anedge map with approximately the same extent as the determined cameraextent at block 307.

Once acquired, the first generated edge map may undergo furtherprocessing to match the dimensions of the extent of the vehicleimage-capture device (e.g. camera FOV). Still referring to FIG. 3, ifthe retrieved edge map has been saved in a compressed format, a featuredecompressor 308 decompresses the compressed map 313. In the case wherethe decompressed edge map does not fully match the camera extent of thevehicle image capture device, then additional edge maps or portions ofedge maps stored in the edge map database may be synthesized to matchthe camera extent. Thus, after optional feature decompression, theprojective transformer 309 can be configured to transform the edge mapportions onto the camera FOV. Alternatively, the dimensions of thecamera extent could be imposed on the edge map, for example via aninverse projective transformation. The generated edge map (now havingdimensions that match the vehicle camera FOV) is effectively projectedonto the FOV of the vehicle camera so as to align the database edge mapand the edge map corresponding to the image captured by the vehiclecamera.

Referring back to FIG. 6, at a step 604 a second value of an edgedetection algorithm coefficient corresponding to the first generatededge map is determined. The second value relates to an intrinsic featureof the image. For example, in one embodiment, the second value relatesto the ground sample distance of the image. If Canny edge detection isperformed on the image, then the coefficient is a sigma (σ) value of theimage that is based at least in part on the second value.

Process 600 then continues to a step 605, where an edge detectionalgorithm is adjusted based on the first and second values of the edgedetection coefficient. In an embodiment, the first and second valuescorrespond to ground sample distances of the respective images, and theedge detection algorithm is adjusted based on the ground sampledistances. For example, the edge detection algorithm can be a Canny edgedetection algorithm, and the edge detection coefficient corresponds tosigma (σ) values of the first generated edge map and the image acquiredby the vehicle image capture device. The edge detection algorithm canthen be adjusted by a ratio of the sigma values given by the followingequation (hereinafter “Equation 2”):

$\sigma_{camera} = {\frac{GSD_{map}}{GSD_{cam}}\sigma_{map}}$

where σ_(camera) is the Gaussian blur sigma coefficient to be used togenerate an edge map of the vehicle camera image, σ_(map) is theGaussian blur sigma coefficient used to generate an edge map of thesatellite map image, GSD_(cam) is the ground sample distance of thevehicle camera, and GSD_(map) is the ground sample distance of thesatellite. Because satellite edge maps can be at predetermined GSDs(e.g., 0.5 m², 1 m², 2 m², 4 m², and 8 m² pixel sizes) for differentairplane altitudes, and because the vehicle may be at an altitude thatrenders an image resolution in between the satellite edge mapresolutions, σ_(camera) for the Canny Gaussian filter used to generatean edge map in response to the camera image is scaled relative toσ_(map) to give more compatibility between edges extracted from thecamera image and edges extracted from the remote sensing database image.Accordingly, the embodiments disclosed herein need not require equalcoefficient values, and thus ground sample distance, between thecompared images. Even if the ground sample distances are not equal,scaling the one or more terms of the edge-detection algorithm increasesthe compatibility of the edges detected in the vehicle-acquired imagewith the edges in the stored database image.

Once the edge detection algorithm has been adjusted, the adjusted edgedetection algorithm can then detect edges in the vehicle acquired image.Referring now to FIG. 3, edge extractor 304 is configured to detectedges using the adjusted edge detection algorithm on the camera image302. Then, a Fourier correlator 310 is configured to performconventional Fourier correlation to compare edge features in the firstgenerated edge map to the edge features extracted from the camera image.A second edge map is generated based on the extracted edge features,which is illustrated in FIG. 6 at a step 607. From there, ashifter/rotator/scaler circuit 311 is configured to estimate vehicleposition data based on the second edge map. The circuit 311 isconfigured to align the edge map generated from the acquired image withthe edge map generated from the remote sensing image to see if the edgesfrom both maps “line up” with one another. The position estimation canthen be stored (e.g. in memory circuitry) for further processing.

Referring now to FIG. 6, process represented by the flow diagram 600terminates after a step 606, during which circuitry (e.g., amicroprocessor or microcontroller) of a navigation system computes,corrects, or both computers and corrects at least one navigationparameter in response to the comparison of the first, remote sensingdatabase edge map, and the second, vehicle-camera-image edge map. Forexample, the circuitry uses data from the comparison of the edge maps tocorrect initial estimates of navigation position information that wereestimated, e.g., with a Kalman filter, and possibly to update theestimation algorithm, e.g., to update one or more coefficients of theKalman filter matrices. Such navigation information (e.g. parameters)include lateral position data (pitch, roll, yaw), heading, altitude, ormovement (velocity and acceleration) of the vehicle.

One embodiment of a circuit configured to implement a comparisonalgorithm for comparing edge features of a remote sensing image and acamera image to determine and/or correct navigation data is illustratedin FIG. 7. The circuit 700 is configured to implement an algorithm thattransforms database edge image 701 and camera edge image 702 to thefrequency domain via transformers 703 and 704, respectively. Thecoordinates are expressed as log-polar coordinates in the frequencydomain (by circuits 705 and 706) before a second set of fast Fouriertransforms via transformers 707 and 708. A phase correlator 709 thenperforms a phase correlation algorithm (described further in FIG. 8) tothe outputs of 707 and 708, followed by an inverse fast Fouriertransform at transformer 710. Next, a filter and peak detector 711compares the output of transformer 710 with the output from transformer703, filters the two inputs, and identifies peaks corresponding to thewaveforms. Filter and peak detector 711 is further configured togenerate a first output, shown as output 1, in response to thecomparison. Output 1 represents correction estimates to rotation andscale navigation parameters.

Still referring to FIG. 7, the camera edge image 702 (and the output ofpeak detector 711) are input into transformer 712, which is configuredto perform an affine transform. A transformer 713 performs a fastFourier transform on the camera edge image, which is input into phasecorrelator 714 along with the output from transformer 703. The output isfed into transformer 715, which is configured to perform an inverse fastFourier transform. A filter-and-peak detector 716 enhances the edges ofthe output from transformer 715, and outputs this enhanced edge map as asecond output, shown as output 2, which represents correction estimatesto translation navigation parameters. When a magnitude of a spatialdifference between output 1 and output 2 is at a minimum, this indicatesalignment of the camera edge map with the database edge map. Therefore,the circuit 700 is configured to operate on different orientations ofthe camera edge map 702 or of the database edge map 701 until a minimumspatial difference is found.

A second exemplary embodiment of a circuit configured to implement acomparison algorithm for comparing edge features of a remote sensingimage and a camera image to correct navigation data is illustrated inFIG. 8. Comparison circuitry 800 can be configured to implement thesteps of a Reddy algorithm or of another conventional algorithm. Thecircuit 800 includes rotation-and-scale-estimator circuit 803, which isconfigured to extract the rotation (e.g. heading) and scale (e.g.altitude) data from the remote sensing edge map (shown stored in amemory 802) and the edge image generated in response to thevehicle-camera image (shown stored in memory 801). Circuit 800 alsoincludes translation correlator circuit 821. As shown in FIG. 8, thecircuitry 800 includes Tukey filter 805, discrete Fourier transformer806, fast Fourier transform shifter 807, magnitude calculator 808,high-pass filter 809, and log-polar remapper 810. The partiallyprocessed edge maps are then processed by another Tukey filter 812,discrete Fourier transformer 813, phase correlator 814, inverse discreteFourier transformer 815, fast Fourier transform shifter 816, Gaussianblur 817, and maximum-value determiner 818. After a rotation converter819 and affine transformer 820 further process the signal(s) output fromthe max-value determiner 818, the processed images then undergo furtherprocessing through translation correlator circuit 821, which includesanother Tukey filter 822, another discrete Fourier transformer 823,phase correlator 824, inverse discrete Fourier transformer 815, fastFourier transform shifter 816, Gaussian blur 817, and maximum-valuedeterminer 818. An output circuit 820 is configured to then generate anoutput 829 indicative of updated navigation information (e.g. rotation,scale, and/or translation parameters) that can be used to correctinitial inertial navigation measurements and values.

FIGS. 9A and 9B are described above.

FIG. 10 is a diagram of a system 1000, which includes a vehicle 1001 anda navigation subsystem 1002, according to an embodiment. The navigationsubsystem 1002 can include one or more of the circuits 300, 700, and 800of FIGS. 3, 7, and 8, respectively, or any other suitable circuit,configured to implement an embodiment of the above-described algorithm(e.g., as described by the flow diagrams 500 and 600 of FIGS. 5-6) fordetermining a navigation parameter in response to a comparison of adatabase edge map to an edge map generated in response to an imagecaptured with a camera, or other image-capture device, onboard thevehicle 100, for example, an aircraft. The vehicle 1001 may be any typeof land, air, space, water, mobile, or other class of vehicle. Vehicle1001 includes navigation subsystem 1002, which is configured forreceiving, sending, or displaying navigation data. Navigation subsystem1002 includes GNSS receiver 1009 for receiving GNSS data/images andinertial measurement unit 1010 for measuring vehicle position andcalculating an initial estimation of vehicle position. Processor 1003 iscoupled to memory 1004 and correlation circuit 1005, generation circuit1007, and comparison circuit 1008. Memory 1004 is configured to storenavigation data and may also store a terrain database of terrain imagesfor estimating the AGL below a vehicle. Memory 1004 may also beconfigured to store edge maps generated from georeferencedorthorectified remote sensing imagery (e.g. remote sensing database) forcomparison to edge images acquired by a camera onboard the vehicle.Although three separate circuits are described in system 1000, theseparate functions described with respect to each circuit may beintegrated into one circuit or further differentiated into one or moreadditional circuits. Thus, the discrete functions of each circuit aredescribed for ease of description only and not intended to be limiting.

The circuitry presented in navigation subsystem 1002 is designed toperform the scaling, comparing, and other functions as described above.Specifically, correlation circuit 1005 includes circuitry configured toidentify the respective ground sample distances of the remote sensingdatabase image and vehicle-acquired image used to scale the coefficients(e.g. Gaussian blur sigma) of the edge detection algorithm. Thesecoefficients may be sigma values input into a Canny edge-detectionalgorithm, but may also include variables from other such algorithms.Correlation circuit 1005 may also include an image-capture device 1006,which may include a camera or other conventional sensor configured tocapture image data. Additionally, correlation circuit 1005 is configuredto adjust a parameter of the edge-determining algorithm based on thevalues of the corresponding coefficients, such as by scaling the sigmavalues using Equation 2.

Generation circuit 1007 includes circuitry configured to generate atleast one edge in response to the adjustment of the edge detectionalgorithm by correlation circuit 1005. In practice, generation circuit1007 generates an edge map generated with the modified edge-determiningalgorithm in response to the vehicle camera image.

Comparison circuit 1008 includes circuitry configured to determine atleast one navigation parameter (for example heading, altitude, orposition) of the vehicle in response to the compared remote sensingdatabase and vehicle images. Thus, comparison circuit 1008 can use themodified data to perform error calculation and correct the initialestimate of vehicle position calculated by inertial measurement unit1100. This may be done modifying, for example, a Kalman filter based onthe corrected measurements.

Example Embodiments

Example 1 includes a method, comprising: determining a first value of acoefficient of an edge-determining algorithm in response to a spatialresolution of a first image acquired with an image-capture deviceonboard a vehicle, a spatial resolution of a second image, and a secondvalue of the coefficient in response to which the edge-determiningalgorithm generated a second edge map corresponding to the second image;determining, with the edge-determining algorithm in response to thecoefficient having the first value, at least one edge of at least oneobject in the first image; generating, in response to the determined atleast one edge, a first edge map corresponding to the first image; anddetermining at least one navigation parameter of the vehicle in responseto the first and second edge maps.

Example 2 includes the method of Example 1, wherein: theedge-determining algorithm includes a Canny edge-determining algorithm;and the coefficient is a σ of the Canny edge-determining algorithm.

Example 3 includes the method of any of Examples 1-2, wherein the atleast one navigation parameter includes one or more of a position, aheading, a height above ground level, and an altitude of the vehicle.

Example 4 includes the method of any of Examples 1-3, wherein thevehicle includes an aircraft.

Example 5 includes the method of any of Examples 1-4, furthercomprising: comparing the first and second edge maps; and whereindetermining at least one navigation parameter includes determining atleast one navigation parameter of the vehicle in response to thecomparing.

Example 6 includes the method of any of Examples 1-5, furthercomprising: determining a difference between the first edge map and thesecond edge map; and wherein determining at least one navigationparameter includes determining at least one navigation parameter of thevehicle in response to the determined difference.

Example 7 includes the method of any of Examples 1-6, furthercomprising: determining an alignment of the first edge map relative tothe second edge map yielding a value of a correlation above a threshold;and wherein determining at least one navigation parameter includesdetermining at least one navigation parameter of the vehicle in responseto a difference between an original position represented by the firstedge map and a position represented by the aligned first edge map andthat yields the alignment.

Example 8 includes a non-transient computer readable medium storinginstructions that, when executed by a computing circuit, cause thecomputing circuit, or another circuit under control of the computingcircuit: to determine a first value of a coefficient of anedge-determining algorithm in response to a spatial resolution of afirst image acquired with an image-capture device onboard a vehicle, aspatial resolution of a second image, and a second value of thecoefficient in response to which the edge-determining algorithmgenerated a second edge map corresponding to the second image; todetermine, with the edge-determining algorithm in response to thecoefficient having the first value, at least one edge of at least oneobject in the first image; to generate, in response to the determined atleast one edge, a first edge map corresponding to the first image; andto determine at least one navigation parameter of the vehicle inresponse to the first and second edge maps.

Example 9 includes the non-transient computer readable medium storinginstructions of Example 8, wherein: the edge-determining algorithmincludes a Canny edge-determining algorithm; and the coefficient is a σof the Canny edge-determining algorithm.

Example 10 includes the non-transient computer readable medium storinginstructions of any of Examples 8-9, wherein the at least one navigationparameter includes one or more of a position, a heading, a height aboveground level, and an altitude of the vehicle.

Example 11 includes the non-transient computer readable medium storinginstructions of any of Examples 8-10, wherein the vehicle includes anaircraft.

Example 12 includes the non-transient computer readable medium storinginstructions of any of Examples 8-11, wherein the computing circuit, oranother circuit under control of the computing circuit, is furtherconfigured: to compare the first and second edge maps; and wherein todetermine at least one navigation parameter includes determine at leastone navigation parameter of the vehicle in response to the comparison.

Example 13 includes the non-transient computer readable medium storinginstructions of any of Examples 8-12, wherein the computing circuit, oranother circuit under control of the computing circuit, is furtherconfigured: to determine a difference between the first edge map and thesecond edge map; and wherein to determine at least one navigationparameter includes determine at least one navigation parameter of thevehicle in response to the determined difference.

Example 14 includes a navigation subsystem, comprising: a first circuit,configured: to determine a first value of a coefficient of anedge-determining algorithm in response to a spatial resolution of afirst image acquired with an image-capture device onboard a vehicle, aspatial resolution of a second image, and a second value of thecoefficient in response to which the edge-determining algorithmgenerated a second edge map corresponding to the second image, todetermine, with the edge-determining algorithm in response to thecoefficient having the first value, at least one edge of at least oneobject in the first image; a second circuit, configured to generate, inresponse to the determined at least one edge, a first edge mapcorresponding to the first image; and a third circuit, configured todetermine at least one navigation parameter of the vehicle in responseto the first and second edge maps.

Example 15 includes the navigation subsystem of Example 14, wherein thefirst circuit includes a camera.

Example 16 includes the navigation subsystem of any of Examples 14-15,wherein the first circuit is further configured to determine a firstvalue of a coefficient of an edge-determining algorithm by including,with the edge-determining algorithm: a Canny edge-determining algorithm;and the coefficient is a σ of the Canny edge-determining algorithm.

Example 17 includes the navigation subsystem of any of Examples 14-16,wherein the at least one navigation parameter includes one or more of aposition, a heading, a height above ground level, and an altitude of thevehicle.

Example 18 includes the navigation subsystem of any of Examples 14-17,wherein the third circuit is further configured to compare the first andsecond edge maps; and wherein to determine at least one navigationparameter includes determine at least one navigation parameter of thevehicle in response to the comparing.

Example 19 includes the navigation subsystem of any of Examples 14-18,wherein the third circuit is further configured to determine adifference between the first edge map and the second edge map; andwherein to determine at least one navigation parameter includesdetermine at least one navigation parameter of the vehicle in responseto the determined difference.

Example 20 includes the navigation subsystem of any of Examples 14-19,wherein the third circuit is further configured to: to determine analignment of the first edge map relative to the second edge map yieldinga value of a correlation above a threshold; and wherein to determine atleast one navigation parameter includes determine at least onenavigation parameter of the vehicle in response to a difference betweenan original position represented by the first edge map and a positionrepresented by the aligned first edge map and that yields the alignment.

From the foregoing it will be appreciated that, although specificembodiments have been described herein for purposes of illustration,various modifications may be made without deviating from the spirit andscope of the disclosure. Furthermore, where an alternative is disclosedfor a particular embodiment, this alternative may also apply to otherembodiments even if not specifically stated. In addition, any describedcomponent or operation may be implemented/performed in hardware,software, firmware, or a combination of any two or more of hardware,software, and firmware. Furthermore, one or more components of adescribed apparatus or system may have been omitted from the descriptionfor clarity or another reason. Moreover, one or more components of adescribed apparatus or system that have been included in the descriptionmay be omitted from the apparatus or system.

What is claimed is:
 1. A method, comprising: determining a first valueof a coefficient of an edge-determining algorithm in response to aspatial resolution of a first image acquired with an image-capturedevice onboard a vehicle, a spatial resolution of a second image, and asecond value of the coefficient in response to which theedge-determining algorithm generated a second edge map corresponding tothe second image; determining, with the edge-determining algorithm inresponse to the coefficient having the first value, at least one edge ofat least one object in the first image; generating, in response to thedetermined at least one edge, a first edge map corresponding to thefirst image; and determining at least one navigation parameter of thevehicle in response to the first and second edge maps.
 2. The method ofclaim 1, wherein: the edge-determining algorithm includes a Cannyedge-determining algorithm; and the coefficient is a σ of the Cannyedge-determining algorithm.
 3. The method of claim 1, wherein the atleast one navigation parameter includes one or more of a position, aheading, a height above ground level, and an altitude of the vehicle. 4.The method of claim 1, wherein the vehicle includes an aircraft.
 5. Themethod of claim 1, further comprising: comparing the first and secondedge maps; and wherein determining at least one navigation parameterincludes determining at least one navigation parameter of the vehicle inresponse to the comparing.
 6. The method of claim 1, further comprising:determining a difference between the first edge map and the second edgemap; and wherein determining at least one navigation parameter includesdetermining at least one navigation parameter of the vehicle in responseto the determined difference.
 7. The method of claim 1, furthercomprising: determining an alignment of the first edge map relative tothe second edge map yielding a value of a correlation above a threshold;and wherein determining at least one navigation parameter includesdetermining at least one navigation parameter of the vehicle in responseto a difference between an original position represented by the firstedge map and a position represented by the aligned first edge map andthat yields the alignment.
 8. A non-transient computer readable mediumstoring instructions that, when executed by a computing circuit, causethe computing circuit, or another circuit under control of the computingcircuit: to determine a first value of a coefficient of anedge-determining algorithm in response to a spatial resolution of afirst image acquired with an image-capture device onboard a vehicle, aspatial resolution of a second image, and a second value of thecoefficient in response to which the edge-determining algorithmgenerated a second edge map corresponding to the second image; todetermine, with the edge-determining algorithm in response to thecoefficient having the first value, at least one edge of at least oneobject in the first image; to generate, in response to the determined atleast one edge, a first edge map corresponding to the first image; andto determine at least one navigation parameter of the vehicle inresponse to the first and second edge maps.
 9. The non-transientcomputer readable medium storing instructions of claim 8, wherein: theedge-determining algorithm includes a Canny edge-determining algorithm;and the coefficient is a a of the Canny edge-determining algorithm. 10.The non-transient computer readable medium storing instructions of claim8, wherein the at least one navigation parameter includes one or more ofa position, a heading, a height above ground level, and an altitude ofthe vehicle.
 11. The non-transient computer readable medium storinginstructions of claim 8, wherein the vehicle includes an aircraft. 12.The non-transient computer readable medium storing instructions of claim8, wherein the computing circuit, or another circuit under control ofthe computing circuit, is further configured: to compare the first andsecond edge maps; and wherein to determine at least one navigationparameter includes determine at least one navigation parameter of thevehicle in response to the comparison.
 13. The non-transient computerreadable medium storing instructions of claim 8, wherein the computingcircuit, or another circuit under control of the computing circuit, isfurther configured: to determine a difference between the first edge mapand the second edge map; and wherein to determine at least onenavigation parameter includes determine at least one navigationparameter of the vehicle in response to the determined difference.
 14. Anavigation subsystem, comprising: a first circuit, configured: todetermine a first value of a coefficient of an edge-determiningalgorithm in response to a spatial resolution of a first image acquiredwith an image-capture device onboard a vehicle, a spatial resolution ofa second image, and a second value of the coefficient in response towhich the edge-determining algorithm generated a second edge mapcorresponding to the second image, to determine, with theedge-determining algorithm in response to the coefficient having thefirst value, at least one edge of at least one object in the firstimage; a second circuit, configured to generate, in response to thedetermined at least one edge, a first edge map corresponding to thefirst image; and a third circuit, configured to determine at least onenavigation parameter of the vehicle in response to the first and secondedge maps.
 15. The navigation subsystem of claim 14, wherein the firstcircuit includes a camera.
 16. The navigation subsystem of claim 14,wherein the first circuit is further configured to determine a firstvalue of a coefficient of an edge-determining algorithm by including,with the edge-determining algorithm: a Canny edge-determining algorithm;and the coefficient is a σ of the Canny edge-determining algorithm. 17.The navigation subsystem of claim 14, wherein the at least onenavigation parameter includes one or more of a position, a heading, aheight above ground level, and an altitude of the vehicle.
 18. Thenavigation subsystem of claim 14, wherein the third circuit is furtherconfigured to compare the first and second edge maps; and wherein todetermine at least one navigation parameter includes determine at leastone navigation parameter of the vehicle in response to the comparing.19. The navigation subsystem of claim 14, wherein the third circuit isfurther configured to determine a difference between the first edge mapand the second edge map; and wherein to determine at least onenavigation parameter includes determine at least one navigationparameter of the vehicle in response to the determined difference. 20.The navigation subsystem of claim 14, wherein the third circuit isfurther configured to: to determine an alignment of the first edge maprelative to the second edge map yielding a value of a correlation abovea threshold; and wherein to determine at least one navigation parameterincludes determine at least one navigation parameter of the vehicle inresponse to a difference between an original position represented by thefirst edge map and a position represented by the aligned first edge mapand that yields the alignment.